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Monetary Convergence Across the Economic Community of West
African States: Observing Convergence as Patterns
Author: David Alemna
The University of Portsmouth, United Kingdom
Email: david.alemna@port.ac.uk
Since its inauguration, the Economic Community of West African States has stressed its desire to advance regional
integration through the establishment of a common single currency (the Eco). This policy has been considered advantageous
given the economic benefits derived from the existence of one of the oldest sub-regional monetary unions across French-
speaking West African Economies. For this reason, the West African Monetary Zone was created as a suggested second
monetary zone consisting of English-speaking countries in the region in anticipation that in the long run, the two would
converge. While empirical studies into the feasibility of achieving monetary integration in West Africa have provided some
understanding of causal notions and possible effects, very few studies embrace complexity theory or attempt to use
complexity-related conceptual notions in the identification and interpretation of patterns produced in longitudinal
applications. Using both empirical and theoretical methods, this paper provides a unique longitudinal application of Dynamic
Patterns Synthesis as an exploratory tool for observing the potential complexities that the proposed single currency
arrangement across West Africa is likely to pose. The findings highlight multiple conjunctural causation in observing
convergence and unpredictability across the Monetary Zone. These observations suggest more time is needed to achieve an
established single currency.
Keywords: Complexity; Dynamic Pattern Synthesis; Monetary Convergence; ECOWAS
Introduction
Following the creation of the European Monetary Union, there has been a growing interest in
regional monetary unification arrangements (Nkwatoh, 2018; Mati, Civcir, and Ozdeser, 2019). It is
suggested that, in unified regional economies, member states can reduce risks amongst vulnerable
economies, minimise wars, encourage intra-regional trade and allow complementarities, and entrench
competitiveness across member states (Mogaji, 2017). This is seen to accelerate economic
development and further improve living standards through trade stimulation (Abban, 2020). Over
time, member states would be able to attract investment and deal with asymmetric shocks (such as
those resulting from unsustainable debt or fluctuations in export commodity prices), as opposed to a
nationalistic approach that may leave a nation vulnerable (Mundell, 1961). Indeed, the IMF (2005)
indicated that the economic benefits of monetary cooperation are highlighted in the fact that 52 of its
184 members participate in such arrangements.
In West Africa, earlier attempts at currency cooperation date as far back as 1912 with the
formation of the West African Currency Board under British colonial rule, which collapsed after
decolonisation. Following the establishment of the Economic Community of West African States
(ECOWAS) in 1975, ECOWAS has stressed its desire to advance regional integration through the
establishment of a common single currency (the Eco). With the existence of an already established
monetary union across French-speaking West African economies (Union Economique et Monetaire
Ouest Africaine—WAEMU 1), a two-track system was proposed (De Grauwe, 2020). This led to the
creation of the West African Monetary Zone (WAMZ) as a suggested second monetary zone consisting
of English-speaking countries in the region in anticipation that in the long run, the two would
converge.
The two-track approach would suggest that monetary integration may not be linear across
ECOWAS members. ECOWAS economies are broadly considered to have relatively weak and
insufficiently diverse economies in certain cases. The region also has a legacy of political instability
1 The West African Economic and Monetary Union (WAEMU) is also known as Union conomique et Montaire Ouest-
Africaine (UEMOA). It created the Communaut Francaise d’Afrique (CFA) Franc, which to date is still in use under the
West African Economic and Monetary Union (WAEMU).
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and poor governance (Okafor, 2017). These dynamics are seen to impact inflation and monetary policy
responses (Carmignani, 2003). Significant variations in existing currency regimes across WAMZ
members also suggests a need for strong regulatory frameworks. These countries would have to
undergo gradual structural and regulatory reforms in order to surrender their sovereignty and
monetary autonomy, and eventually adopt the Eco. In some instances, their currencies may lose the
competitive advantage they may have derived from the current situation. After a series of delays in
earlier attempts to adopt the Eco (from 2005 to 2009 to 2015 to 2020 and now to 2027), it is clear that
the complexities surrounding the West African monetary integration programme may seem more
profound than previously anticipated. Evidently, there is a need to reflect on previous experiences
within the region to draw lessons for future efforts.
While Mogaji (2017) studied the evaluation of macroeconomic indicators and the dynamics for
monetary integration of West Africa—focusing specifically on WAMZ—those who attempt to assess
ECOWAS convergence have focused on expansions in intra-trade relations and the possible effects of
a currency union (see for instance, Ezekwesili, 2011; Abban, 2020; Ilyas, et. al., 2021). Alagidede,
Coleman, and Cuestas (2012) applied fractional integration and cointegration econometric modelling
to study the behaviour of inflation among WAMZ countries and discovered the existence of substantial
heterogeneity. Houssa (2008) utilised a dynamic factor model to observe the asymmetric shocks of
monetary union in West Africa and found that French-speaking countries experience more demand
shocks. Through the use of correlation analysis, Fielding, Lee, and Shields (2004) found that the extent
of macroeconomic integration across CFA Franc members is encouraged by monetary integration.
Others have also applied Optimum Currency Area to examine the preparedness of ECOWAS members
to form a monetary union and found that such aspirations may be impossible (Nkwatoh, 2018; Mati,
et. al., 2019).
While these important studies have provided an understanding of causal notions and possible
effects, very few studies embrace complexity theory or attempt to use such conceptual notions in the
identification of patterns produced in longitudinal applications. However, a non-linear perspective that
applies complexity related methodologies may be more fitting in observing how variables and cases
interact over time (McEvoy and Richards, 2006; Zelli, Gerrits, and Möller, 2021). Complexity theory
provides more thorough insights and observation of trajectories over time. The patterns that evolve are
also useful in understanding the nature of complexity within the monetary union. Identifying these
patterns over time also offers a degree of system predictability (order or chaos) and observed macro
stability (convergence). To advance the observation of these complex patterns, this paper utilizes
complexity theory as a methodological tool for understanding how the two monetary trajectories can
be observed during five historical time points.
Monetary Convergence Across ECOWAS – A Complexity Perspective
Studies into the feasibility of achieving monetary convergence have revolved around the
Optimum/Optimal Currency Area (OCA) theory. This theory holds that a country anticipating
membership into a monetary union should satisfy a set of criteria as a condition for assimilation (Hsu,
2010; Regmi, Nikolsko-Rzhevskyy and Thornton, 2015). OCA is grounded on the concept of sigma
convergence. Here, convergence is viewed as a decrease in the dispersion index of a variable across a
group of cases over time (Hammouda et. al., 2009). Thereby demonstrating similar patterns of
economic behaviour (for instance, in inflation expectancies and long-term interest rates). Empirical
research on the macroeconomic effects of convergence often focus their analysis on a ‘growing
resemblance’ to identify processes of catching up (beta-convergence), growing similarities (sigma-
convergence), or movement towards exemplary models (delta-convergence) (Heichel, et. at., 2005).
Such categorisation of cases into restricted parameters for observing case mobility can be limiting.
Although convergence may occur over time, case movement may be haphazard and irregular.
In applying complexity theory, a social researcher sees society as dynamic, and not static. The
predominant characteristics of this dynamism are the interactions that may transpire between and
within systems. Over time, these interactions may produce patterns (Haynes, 2017). Nevertheless,
although in a state of perpetual chaos and disorder, patterns may also result over time in societies
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quest for order. In macro-political economics, these changes occur in inconsistent ways at the micro,
meso, and macro levels. Changes could be triggered by endogenous factors (e.g., changes in
government spending) or as a result of external factors (e.g., changes in aggregate demand for export
commodities) (see for instance Stone, 2008). As such, there are multiple interactions between and
within systems. In this complex system, “the causal categories become intertwined in such a way that
no dualistic language of state plus dynamic laws can completely describe it” (Rosen, 1987.p. 324).
Bovaird (2008) also describes a complex system as a succession of subsystems. These complex systems
are seen to exist with their own rules of behaviour, have external forces to deal with, and interact with
each other (Klijn, 2008).
From this perspective, multiple systems can be identified in the context of ECOWAS currency
convergence. A country can be regarded as a system having its own domestic policy values, priorities,
and goals, inter alia, which define its interactions with other systems. In this country, numerous
systems exist and interact with each other at all levels. From a broader perspective, the country also
becomes a component of a larger system (in this case, a regional block—ECOWAS) that interacts with
other systems. This picture becomes even more complex when one considers interactions at the
continental and global levels (Cilliers, 1998; Nanz & Steffek, 2004; Stone, 2008)2. In this research,
multiple systems are seen in the form of countries and their monetary systems (Howlett & Ramesh,
2002).
Figure 1 (below) attempts to provide a graphical representation of the complex interactions
observed in the ECOWAS monetary system. From this, it is evident that each system is bounded by a
dotted line. This suggests that systems are semi-permeable and factors within a system may influence
activities within another system. This emphasises the assumption that no smaller social system exists
in isolation and as such, they may have an influence on each other—and are also influenced by their
external environment (Cilliers, 1998).
Figure 1. Complex interactions in the ECOWAS monetary system (developed by author).
Indeed, highlighted in the literature on currency convergence, in order to reduce complexity
in monetary transactions and eliminate exchange rate uncertainty, among other things, countries
adopt monetary cooperation (De Grauwe, 2000). Here, governments attempt to create “order” by
identifying certain standards they seek to achieve over time. This is done in anticipation that, in the
long run, their monetary systems can become more alike and merge into one larger system. For this
reason, monetary cooperation requires a common “single” currency, a common central bank, and a
common monetary policy (Plasmans et al., 2006)—thereby reducing the complexity in monetary
transactions.
2 Other systems also include intergovernmental cooperation, nongovernmental organizations, and market inflows that
influence trade, labour, and capital flows into countries.
Positiveand
Negative
Feedback
Country B1Country C1
Country A1
Country D1
Country B Country C
Country A
Country D
Positiveand
Negative
Feedback
ECOWAS
Africa
Global
WAMZ
WAEMU
Humanand
Financial Capital
Markets
Global
Governance
and policies
Externalities
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The implementation of a convergence criterion is seen to promote aspects of certainty and
stability, and to limit instability. While attempting to create order, patterns may emerge as a result of
the interactions between and within cases, as well as their externalities over time. In such instances,
the task is to understand the various journeys the cases may take over time, the patterns that may
emerge, and how similar or different cases become. For such observations, causality becomes
unpredictable as policy outcomes may vary across similar cases or outcomes may appear similar in
divergent cases (Ragin, 1997; Byrne, 2002; Ragin, & Rihoux, 2009, Haynes, 2017). The interdisciplinary
approach to the application of complexity has provided some conceptualisation of certain features that
can be used to describe complex systems based on the properties they may exhibit (HMT, 2020). To
contain the research within a manageable scope, focus is contextually placed on some fundamental
temporal elements of complex systems that tend to be missed in the literature on ECOWAS
convergence.
Observing Complex Dynamic Patterns Over Time
Neoclassical models on long-run macroeconomic convergence have focused on a growing
similarity across a range of aggregate scores. Such methodological applications sometimes tend to be
skewed by outliers or misrepresent changes in variable distribution across cases (Haynes and Haynes,
2016). It is for this reason that some scholars have highlighted the crucial role the spatial and time
dimensions play in observing convergence (Knill, 2005; Heichel, et. al., 2005). For instance, in their
assessment of convergence and heterogeneity across Euro-based economies, Haynes and Haynes
(2016) provide some limitations in statistical techniques used to assess convergence. These scholars
have highlighted the complex notion of observed convergence and challenged traditional reductionist
models that tend to ignore the context and case sensitive nature of this growing similarity.
For some complexity scientists, the notion of a system moving from one stable state to another
as a result of change is flawed. Instead, complex adaptive systems are typically non-linear and highly
sensitive to initial conditions (Stacey, 1996). Here, the context within which interactions occur within,
and between, systems at specific time points form the initial conditions for the emergence of future
order. While monetary convergence turns to observe an aggregate growth in similarity between
currency regimes over time, an application of complexity theory enables the researcher to observe the
case-sensitive nature of convergence over time. For instance, in cases where multiple causalities are
repeatedly reinforced by experiences associated with circular causality, when “effects” are fed back to
alter “causes,” a social researcher may observe the presence of feedback, feedback loops, or complex
recurring patterns that may prompt logical paradoxes (Smith, 2005).
Feedback can be explained as the process in which a part of the response of a system is fed
back to a cause (Martínez-García, and Hernández-Lemus, 2013). This could result in recursive “loops.”
Positive feedback increases dynamics or the divergence of a system to its original state, thus
destabilizing the system. Negative feedback on the other hand does the opposite (Maturana, 1980).
Contextually, periods of stability of government policies towards the achievement of a convergence
criteria may come with occasional (and often abrupt) changes/events3. These changes may be
explained by the presence of positive and negative feedback processes resulting from interactions with
other systems, or with the external environment. Such interactions could disrupt convergence as
national specific policy interventions may produce varying outcomes. In response to negative events,
such as the Ebola outbreak or the Covid-19 pandemic, a country may forgo its ambition to meet a
convergent standard in order to respond to domestic impacts. Additionally, nationalistic approaches
to economic recovery could impact convergence. If this is understood, recognising self-organising
patterns may result in better understanding and control of complex systems, and aid in the allocation
of resources to combat such impacts (Baumgartner and Jones, 2002; Martínez-García, and Hernández-
Lemus, 2013).
Given that complex systems can be sensitive to initial conditions, and as such, interactions
within and between these complex systems could result in feedback loops, emergent complex patterns
3 Nelson & Winter (1982) categorizes three types of events to understand the subtle differences between pure chance and
apparent chance. These are Random, Unpredictable, and Deliberate.
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may develop over time (Smith and Jenks, 2006). New, unexpected features may start to develop over
time from interactions between and within systems (Haynes, 2017). These patterns are not simple
predictable outcomes, but complex patterns that may result from interactions within a given context
and at a specific point in time. In the case of monetary convergence, these patterns should appear,
provided that member states achieve the convergence criteria4. To observe these longitudinal
interactions within the ECOWAS monetary system, this paper employs a new methodological tool that
provides systematic comparative observations of variable and case patterns over time.
Methods and Data
Since this article integrates aspects of macro-political economy with complexity theory in its
analysis of monetary convergence, an application of Dynamic Pattern Synthesis is utilised as an
alternative methodological approach to the assessment of convergence. This is aimed at advancing the
development of a new, combined longitudinal case-based configurational method. Dynamic Pattern
Synthesis can be used as an exploratory tool to observe complex patterns within a system over time. It
combines the use of hierarchical cluster analysis (HCA) with configurational modelling to examine
similarities and differences between cases at different time points, as well as the extent to which such
cases remain similar or different over time (Haynes, 2017).
HCA is used as an exploratory tool to observe patterns of similarity and differences across
cases. It provides a focus on countries that maintain cluster grouping over time while highlighting
cases that may appear at the periphery of case clustering (outliers). In longitudinal analysis, Dynamic
Pattern Synthesis can also be used to identify the movement of cluster partnerships over time and the
changes that may emerge based on the variables utilised in the modelling. The application of case-
based configurational modelling also allows for careful observation of the trajectory of individual cases
and the effects of variable influences on cluster groupings over time.
In this study, Dynamic Pattern Synthesis is applied to cases consisting of fifteen countries
within the ECOWAS region in the period 2012-20165. WAEMU members consist of eight economies:
Benin, Burkina Faso, Cote d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal and Togo. These countries
use the CFA franc currency pegged to the Euro. However, unlike the WAEMU, members of the WAMZ
have their individual currency regimes—WAMZ consists of six West African states: Cabo Verde,
Gambia, Ghana, Guinea, Liberia, Nigeria, and Sierra Leone. The period under analysis (2012 - 2016)
was also chosen to reflect the possible impact of an event (the Ebola crisis).
ECOWAS assesses monetary convergence on the attainment of specific macroeconomic
indicators and structural adjustments that illustrate a threshold level of convergence to become
members of the Union6. This criterion contains two categories, primary and secondary (see Table 1.).
The primary criteria consist of mandatory standards that members must meet to be integrated into the
union, while the secondary criteria are desirable standards, but not mandatory. In this study, these
markers are used as ‘input descriptors’ to assess convergence: that is to say, certain features of a system
that can make this system complex (Zelli, et. al., 2021). These indicators highlight some relational
qualities and timepoints that represent more than the sum of a system’s components, thereby defying
reductionist techniques. Data on each indicator was collected from the most recent ECOWAS
Convergence Report (ECOWAS, 2017a). This report provided the most comprehensive dataset on the
ECOWAS convergence situation during the period under discussion. The complete dataset can be
found in Appendix 1.4. Indicators are measured as per the outlined standards in Table 1 below.
4 It is from this perspective that methods like realist evaluation (Pawson and Tilley, 1997) and Qualitative Comparative
Analysis (Rihoux & Ragin, 2009), amongst many others, are employed to evaluate the influences of the context, intervention’s
context, mechanism, and consistencies in outcomes.
5 The period of choice is based on the most recent available online published 2017 ECOWAS Convergence Report (see.
Economic Community of West African States, 2017a). This dataset was also found to be the most comprehensive data on the
convergence situation at that time. While attempts were made by the researcher to update this dataset, data on some cases
could not obtain as some countries do not publicly release data in a timely manner.
6 For a detailed discussion on convergence criteria, Bukowski (2006) provides a detailed overview of the economic theory
behind this.
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Variable Code
Meaning
Standards
Primary Criteria
BD
Ratio of Budget Deficit to Nominal
GDP
≤ 3%
IR
Annual Average Inflation Rate
≤ 10% of target ≤ 5% in 2019
CBF
Central Bank Financing of Budget
Deficit
≤ 10% of tax revenues of n-1
GER
Gross External Reserve
≥ 3months
Secondary Criteria
TPD
Ratio of Total Public Debt to GDP
≤ 70%
NERV
Nominal Exchange Rate Variation
± 10%
Table 1. List of Convergence Criteria, Standards, and Variable Codes
Hierarchical cluster analysis is applied using a combination of indicators on the primary and
secondary convergence criteria (outlined in Table 1). Ward’s linkage was considered to be the most
appropriate clustering proximity method as it produced fewer mathematical artefacts in the data. Input
variables were also standardised to z scores to lessen the impact of variables with greater variance.
Pearson’s correlation was also conducted to observe the possible influences of variables on each other
over time (see Appendix 1.2. for correlation plots using correlograms, see Friendly, 2002). Annex 1.3.
provides some complementary descriptive diagrams highlighting trends across the convergence
indicators.
Cluster dendrograms (Figures 2 - 5 in the results section) offer a visual representation of the
HCA results at each time point and are used as the starting point for observing changes that may
emerge over the period under analysis. In this HCA presentation, cluster dendrograms are placed
alongside each other to highlight emergent patterns across cases over time—cases that remain closely
similar over one time point to another (i.e., contextually, these cases converge over t0 –> t1). The degree
of similarity across clustering is also illustrated by the vertical and horizontal lines that join cases
together. When these lines are represented by dashes or dots, this signifies higher dissimilarities. In
contrast, tick/solid lines represent stronger similarities across cases. It is important to note that, unlike
previous longitudinal applications of Dynamic Pattern Synthesis which have focused on the use of
three-time intervals (see for instance: Haynes & Haynes, 2016; Haynes, 2018; Taylor, Haynes &
Darking, 2020), this paper compares HCA dendrograms across five time points.
Following the observance of case interactions in cluster grouping over time, and applying the
same dataset, configurational case-based modelling is used to identify the influence of associated
variables on consistent and emergent clusters. This also allows for the observation of changes in
variable patterns over time. Configurational case-based modelling approaches such as Qualitative
Comparative Analysis (QCA) have been used in systematic analysis to observe the ways in which the
impacts (outcome) of an intervention can be evaluated by the observance of configurational factors
(see for instance Ragin, 2000; Schneider & Rohlfing, 2016; Ragin & Fiss, 2017; Kaimann, 2017). This
approach recognises varying contributory factors that can lead to change and allows for the observation
of ‘multiple conjunctural causation’ (Ragin, 1987; Berg-Schlosser, et. al., 2009; Haynes, 2017). In this
application, individual variables are converted into dichotomous variables (threshold scores) based on
the achievement of standards set in the convergence criteria. For cases where the achievement of the
convergence criteria is met, data is shaded in the colour of the pattern in the HCA results (see Tables
5 - 10 in Annex 1.1.). Likewise, in cases where this is demonstrated across a set of convergent cases,
indicators are in “Bold” and “Italic.” The Boolean convention of UPPERCASE equals achieved criteria
and lowercase equals did not achieve criteria (Rihoux & Ragin, 2009) is used in the analysis to highlight
longitudinal trends and observe dynamic patterns across variables and cases over time. The results of
the application of Dynamic Pattern Synthesis are presented below.
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Analysis and Results
Hierarchical Cluster Analysis:
Figure 2 (below) provides a comparative analysis of the convergence situation for the first time
interval when the HCA dendrograms for the years 2012 and 2013 are put together. From the left side
(representing 2012 and the right side representing 2013), Sierra Leone, Guinea, and Liberia are
clustered together. However, the dotted line linking Sierra Leone to Guinea and Liberia suggests these
two countries share more similarities. Below these cases, Burkina Faso, Mali, and Nigeria are clustered
together. Similarly, the dotted line linking Burkina Faso and Mali to Nigeria suggests more similarities
between the two cases. At the next stage of clustering, the two lines (a tick/solid line for Sierra Leone,
Guinea, and Liberia and then a dotted line for Burkina Faso, Mali, and Nigeria) linking the two cluster
groups together suggests more similarities between Sierra Leone to Guinea and Liberia as compared
to Burkina Faso, Mali, and Nigeria—in this sense Nigeria can be seen as an outlier. This clustering
sequence is repeated until all cases are grouped. In Figure 2, two larger/broader cluster formations are
also seen to appear in 2012 with a dotted line linking them (i.e., from Sierra Leone to Nigeria and then
from Guinea Bissau to Ghana). This shows that cases demonstrate more similarities within cluster
members as compared to cases outside cluster memberships.
Placing HCA dendrograms alongside each other allows for the observation of case movement
across clustering. For instance, in the top half of Figure 2, the HCA output shows that, while in 2012
Sierra Leone, Guinea, and Liberia shared some similarities, these similarities were stronger between
Guinea and Liberia. However, in 2013 Guinea starts to show more similarities with Sierra Leone and
differences with Liberia. This can aid in the observation of the stability (or otherwise) in the movement
of cases over the period under analysis. In instances where cases remain clustered over time, emergent
patterns begin to appear. For instance, between 2012 and 2013 three convergent clusters (emergent
cases) can be observed (i.e., Guinea Bissau and Cote D'Ivoire; Togo and Senegal; and Cabo Verde,
Gambia, and Ghana). These cases demonstrated strong similarities across the two time points as
compared to the other cases. The same HCA application is repeated across the dataset (i.e., for 2013 -
2014, 2014 - 2015, etc.). The dendrograms for the subsequent time points are represented in Figures
3 to 5. For the purpose of this study, the HCA analysis would place attention on the emergence of case
patterns that appear during the period under discussion.
Table 2 (below) provides a simple presentation of the emergence of case patterns in order of
appearance over the period under discussion. An additional column is added to highlight the currency
used by each country. Colour codes are also applied to highlight emergent case patterns. Based on the
journeys travelled, complex case patterns begin to appear. While some patterns remained consistent
throughout the period under discussion (e.g., Guinea Bissau and Cote D’Ivoire), others faded over
time (e.g., Togo and Senegal). In some cases, the dendrograms may suggest their differences grew
with time (e.g., Gambia, and Ghana). In other cases, convergent case patterns are seen to emerge and
then grow apart over time (e.g., Guinea, Sierra Leone, and Liberia). Nevertheless, over time new
patterns also begin to emerge (e.g., between 2015 - 2016 Burkina Faso and Mali, and Benin and Niger).
This highlights the evolution of the ECOWAS system and its self-organising nature. HCA is used in
this context to group cases on the basis of their similarities to observe case patterns over time. This is
based on the actual values of macroeconomic indicators—not the achievement of the convergence
criteria.
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Currency
2012
2013
2014
2015
2016
CFA
franc
Guinea
Bissau
Guinea Bissau
Guinea
Bissau
Guinea Bissau
Guinea Bissau
CFA
franc
Cote D'Ivoire
Cote D'Ivoire
Cote D'Ivoire
Cote D'Ivoire
Cote D'Ivoire
Naira
Nigeria
Nigeria
CFA
franc
Togo
Togo
CFA
franc
Senegal
Senegal
Escudo
Cabo Verde
Cabo Verde
Dalasi
Gambia
Gambia
Gambia
Gambia
Cedi
Ghana
Ghana
Ghana
Ghana
Franc
Guinea
Guinea
Guinea
Leone
Sierra Leone
Sierra Leone
Sierra Leone
Dollar
Liberia
Liberia
Liberia
CFA
franc
Burkina Faso
Burkina Faso
CFA
franc
Mali
Mali
CFA
franc
Benin
Benin
CFA
franc
Niger
Niger
Table 2. Emergent Case Patterns
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Configurational Model:
Following the observance of case interactions in cluster grouping, and applying the same
dataset, configurational case-based modelling is used to identify associated variable influences on
consistent and emergent clusters at each time point. Variable changes across cases are observed using
the raw scores for each variable in the dataset. This is provided in Tables 5 - 10 (see Annex 1.1).
Complementarily, Table 3 (below) provides a simplistic configurational model used to observe variable
changes that may have influenced the emergence of case patterns over time.
As shown in Table 3 (below), Cote D'Ivoire and Guinea Bissau shared similar variable
characteristics in the achievement of IR, CBF, GER, TPD, and NERV in 2012. Although Nigeria shared
slight similarities in achieving CBF, GER, TPD, and NERV, it was not clustered with Cote D'Ivoire and
Guinea Bissau (see Figure 2). In this year, Nigeria’s annual average inflation rate (IR) was at 12.20%
as compared to the single-digit inflation rates experienced by Cote D'Ivoire and Guinea Bissau. From
2013, Nigeria starts to show more similarities with Cote D'Ivoire and Guinea Bissau as they
demonstrate similar variable characteristics for CBF, GER, TPD, and NERV. These similarities are
further intensified as inflation rates stayed relatively stable in the 2013 - 2015 period. Similarities peak
in 2014 when all three countries met all the convergence criteria. Despite remaining clustered together
in 2015, dissimilarities start to appear as Nigeria is unable to reduce its central bank financing of
budget deficit. This was attributed to a decline in total revenue and grants (ECOWAS, 2016). In 2016,
Cote D'Ivoire and Guinea Bissau showed strong similarities in achieving five of the six criteria. Both
countries were only unable to maintain their Ratio of Budget Deficit to Nominal GDP at a level equal
to, or lower than, 3%. On the other hand, Nigeria was able to achieve BD but underperformed with its
inflation rates and nominal exchange rate variations. The increase in the general level of prices for
Nigeria could be explained by the depreciation of the Naira in 2016 (ECOWAS, 2016). For these cases,
some aspects of feedback can be observed in the emergence and unpredictability of the Nigerian
economy.
Table 3. Configurational Output. Note: Authors computation using Excel. Variable codes: Primary Criteria: BD - Ratio of Budget
Deficit to Nominal GDP; IR - Annual Average Inflation Rate; CBF - Central Bank Financing of Budget Deficit; GER - Gross External
Reserve. Secondary Criteria: TPD - Ratio of Total Public Debt to GDP; NERV - Nominal Exchange Rate Variation. UPPERCASE
equals achieved criteria and lowercase equals did not achieve criteria.
For Togo and Senegal, both cases shared similar variable characteristics in the achievement of
BD, IR, CBF, GER, TPD, and NERV between the years 2012 - 2014. In 2012, differences start to emerge
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in the ratio of public debt to GDP across cases. While Senegal’s TPD dropped by 6% between 2012 and
2013, Togo’s increased by 1.3% (see Table 6 in Appendix 1.1.). This disparity becomes profound in
2014 when Togo’s ratio of debt to GDP rose to 66%. In the subsequent years, this continued to rise -
moving from 76.8% in 2015 to 79.4% in 2016. According to the World Bank-IMF joint debt
sustainability analysis (2019), Togo’s high public debt is due to high deficits, contingent liabilities, and
accumulated arrears amongst others. This has left little space to absorb external shocks. In this case,
some adverse effects of the Ebola outbreak (2013 - 2015) destabilised the economy, demonstrating
some aspects of sensitivity to initial conditions and path dependency in the Togolese economy—
making it ineffective in its ability to absorb external shocks.
Highlighting the complexity within WAMZ, the configurational modelling of Cabo Verde,
Gambia, Ghana, Guinea, Sierra Leone, and Liberia demonstrates complex interactions influencing the
clustering of these economies. For Cabo Verde, Gambia, and Ghana, the HCA results showed that
these cases shared strong similarities in 2012 and 2013. This is partially observed in the achievement
of BD, IR, GER, and NERV in 2012, and BD and GER in 2013. In the subsequent years, Cabo Verde
starts to diverge after failing to meet the convergence criteria for only TPD. While Ghana and Gambia
remain converged, they continue to show some similarities in BD, CBF, GER, TPD, and NERV. With
the Escudo pegged to the Euro, Cabo Verde was able to reach all the primary criteria and stabilize its
nominal exchange rate variation. However, the economy of Cabo Verde is driven by tourism and was
unable to stabilise its ratio of total public debt to GDP due to the adverse effects of the Ebola breakout.
Nyarko, et. al., (2015) indicated that, in Africa, the Ebola crisis affected the travel and tourism sector
almost more than any other sector. This is also emphasised by Rosselló, Santana-Gallego, and Awan
(2017), who looked at the impacts of infectious diseases on international tourism. Evidently, Cabo
Verde had earlier implemented an IMF intervention (2010 - 2012) to manage domestic government
debt, increase international reserves, and promote structural reforms to improve debt management
and strengthen the financial sector (Cabo Verde, 2010). In 2014, Gambia also experienced major
shortages in rainfall, coupled with the Ebola breakout across neighbouring countries resulting
economic shocks drastically increased its central financing of budget deficit in the subsequent years.
Likewise, although the emergence of Guinea, Sierra Leone, and Liberia may also suggest
partial convergence within the region, these countries are predominately members of the Mano River
Union. Nevertheless, the clustering of Guinea, Sierra Leone, and Liberia provides a noteworthy case
of emergence resulting from an event (Nelson and Winter, 1982). The configurational model shows
inconsistencies in meeting all the convergence criteria across the period under study. Yet, these cases
start to show strong similarities in the clustering between 2013 - 2015. During this period, these
countries were hit by the Ebola epidemic. CDC (2020) statistics suggest that they were the hardest hit
countries with the highest death rates. Here, the impact of an epidemic can be seen to have had adverse
effects on the monetary system of these countries. After the Ebola outbreak, Guinea, Sierra Leone, and
Liberia do not cluster in 2016—to some extent highlighting nationalistic approaches to economic
recovery. For Burkina Faso, Mali, Benin, and Niger, these countries met all the convergence criteria
throughout the period under study. A detailed look at the indicators (Table 9 and Table 10 in Appendix
1.1.) would suggest that, over time, they become more similar. For instance, slight variations can be
observed across the indicators for BD, IR, and TPD. Overall, these patterns provide a path
understanding of case variable movement over time—while developing some theories of social change.
The configurational modelling also allows for the observation of ‘multiple conjunctural
causation’ (Ragin, 1987; Berg-Schlosser, et. al., 2009; Haynes, 2017) in the Optimum/Optimal
Currency Area (OCA) theory—which suggests that in satisfying a set of criteria countries begin to show
similar economic patterns over time (sigma convergence). This is slightly evident across CFA franc
members. For instance, Guinea Bissau, Cote D'Ivoire, and Nigeria (uses the Naira) showed similarities
in meeting the convergence criteria in 2013, but these similarities peaked in 2014. Adverse effects are
also observed when countries are unable to meet the convergence criteria (for instance, Guinea Bissau,
Cote D'Ivoire, and Nigeria in 2015 - 2016; and Togo and Senegal in 2014 - 2015). Similarly, for cases
where the achievement of all the convergence criteria was met throughout the period under discussion
(i.e., Burkina Faso and Mali, and Benin and Niger) cases begin to show more similarities over time—
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eventually leading to some form of ‘lock-in’ (Unruh, 2000). These observations, to an extent, provide a
demonstration of the Optimum/Optimal Currency Area (OCA) theory.
The clustering of Cabo Verde also provides some aspects of delta convergence. Despite its
legacy of debt issues, as well as some adverse impacts from the Ebola outbreak on its tourism economy,
Cabo Verde showed better resilience in meeting the primary criteria in the subsequent years as
compared to Gambia and Ghana. This, to an extent, can be attributed to the fact that the Escudo is
pegged to the Euro. Interestingly, during these years (2014 - 2016), Cabo Verde showed more
similarities with WAEMU members (CFA Franc is also pegged to the Euro). This also suggests aspects
of delta convergence (a movement towards an example model—in this case the Euro). Nevertheless,
some systemic spill overs can also be observed when policy actions within one subsystem affects
another subsystem (Howlett & Ramesh, 2002; Williams, 2009). This is seen in the form of global travel
bans as a result of the Ebola outbreak having an impact on Cabo Verde’s ability to meet a convergence
criterion. Other complex interactions are also observed in the form of feedback effects in response to
events—as with the case of the Ebola outbreak in Guinea, Sierra Leone, and Liberia; aspects of
sensitivity to initial conditions in stable patterns observed between some CFA franc members, and
path dependency in Togo’s increasing public debt.
Longitudinal Configurational Model:
In the longitudinal application of Pattern Synthesis, data can also be coded to further highlight
trends in variables and case patterns over time. Table 4 (below) shows a recoded configurational model
highlighting the consistency/stability in achieving each criterion throughout the period under analysis.
The model, to an extent, shows variations in patterns across and within the two monetary systems
(reorder version highlighting disparities across the unions). From this, systemic instability can be
observed within WAMZ—when case and variable patterns seem to be unstable over time. With the
exception of Cabo Verde, patterns in variable trends and case patterns for WAMZ members is relatively
unstable. Very few countries were able to meet all the convergence criteria during the period under
study. In these cases, emergent cluster patterns were not directly observed as a result of achieving the
convergence criteria, but rather as a result of other impacts. Such changes in case patterns are reflected
in the instability of associated variable changes.
Observing Stability in achieving the ECOWAS Convergence Criteria: 2012 - 2016
Union
Country
BD
IR
CBF
GER
TPD
NERV
Variable Pattern
Case Pattern
WAMZ
Gambia
MET
MET
NOT MET
Unstable
Unstable
WAMZ
Ghana
MET
WAMZ
Cabo Verde
MET
MET
MET
MET
NOT MET
MET
Stable with some
conditions unmet
Unstable
WAMZ
Guinea
MET
Unstable
Unstable
WAMZ
Sierra Leone
MET
MET
MET
WAMZ
Liberia
MET
MET
MET
MET
WAMZ
Nigeria
MET
MET
MET
Unstable
Unstable
WAEMU
Cote D'Ivoire
MET
MET
MET
MET
MET
Largely stable
Stable
WAEMU
Guinea Bissau
MET
MET
MET
MET
MET
WAEMU
Burkina Faso
MET
MET
MET
MET
MET
MET
Stable
Initially
unstable but
convergence
was achieved in
2015/2016
WAEMU
Mail
MET
MET
MET
MET
MET
MET
WAEMU
Benin
MET
MET
MET
MET
MET
MET
WAEMU
Niger
MET
MET
MET
MET
MET
MET
WAEMU
Senegal
MET
MET
MET
MET
MET
MET
Largely stable
Initially stable
but diverged
after 2014
WAEMU
Togo
MET
MET
MET
MET
MET
Table 4. Longitudinal Configurational Output (authors computation using Excel).
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However, across WAEMU members, some form of stable dynamics can be observed—where
variables and case patterns tend to remain stable over time. Variable patterns seem to be largely stable
with the achievement of IR, CBF, GER, and NERV across all members throughout the period under
discussion. A few members show inconsistencies in the achievement of BD (Cote D'Ivoire and Guinea
Bissau) and TPD (Togo). In the WAEMU, members also tend to remain clustered over longer periods
and in some cases, the same variables are associated with this clustering. For WAEMU members, the
emergence of case patterns can be somewhat associated with the attainment of the convergence criteria
or to the fact that these countries also have the pre-existing unified institutional and policy structures
needed to ensure policy alignment. In the application of Pattern Synthesis, additional qualitative data
can be used to interpret counterfactuals.
Conclusion: Observing System Change as Patterns
Complexity theory provides a multidimensional perspective to the assessment of monetary
convergence and offers a better understanding and observation of trajectories over time. Case-based
methods, such as Dynamic Pattern Synthesis, allow for the observation of case changes as well as the
variable movements associated with such changes. These patterns are usually unpredictable, resulting
from interactions within a given context and at a specific point in time. Over time, identifying these
patterns offer some degree of system predictability based on the experiences of cases. In its
longitudinal application, repeating Dynamic Pattern Synthesis across the same dataset ensures that
the method theorises social change, thereby assessing changes against the irreversible dimension of
time. This allows for observation of multiple conjunctural causation and consideration of the impact
of events across the Monetary Zone. Combined with additional qualitative data, and knowledge of the
context and cases, a researcher can interpret these patterns.
For instance, in this study, the findings suggest more efforts need to be focused on
convergence across WAMZ members. This is evident in the unstable nature of variables and case
patterns across these economies. With exception of Cabo Verde, no WAMZ member was able to meet
all the primary convergence criteria during the period under discussion. This highlights the need for
stronger institutional and regulatory alignment/compliance across WAMZ members—or what Buti
and Turrini (2015) would term ‘structural convergence.’ If this is targeted towards a focus on
encouraging the implementation of structural reforms to enhance technical preparedness, it is more
probable to create stable patterns within the region—as with the case of WAEMU members.
The DPS method also allows for the exploration of exceptional cases. For instance, in the study,
it becomes evident that Cabo Verdes’ resilience in meeting all the primary criteria can be attributed to
the fact that its currency is pegged to the Euro. This could add to the debate on the use of fixed/floating
exchange rates within the region to minimise risks amongst vulnerable economies (Nathaniel,
Oladiran & Oladiran, 2019). To strengthen regulatory alignment and encourage stable patterns,
especially in cases of negative events (such as the Covid-19 pandemic), national actors can consider
introducing fixed exchange rates—as against the current floating mechanism used amongst most
WAMZ members—to aid in improving alignment and accelerating convergence. However, some
adverse effects of this can, to an extent, become evident in less diversified economies—as reflected in
Cabo Verde’s dependence on tourism and growth in public debt.
External events also impact the convergence situation during the period under discussion. In
such instances, utilising complexity theory, to an extent, can aid in predicting possible impacts of social
change on aspects of a system over time. Evidently, while orthodox approaches may observe
convergence as a ‘growing resemblance’—identifying processes of catching up, growing similarities,
or movement towards exemplary models (Heichel, et. at., 2005). This research demonstrates that,
although convergence may occur over time, case and variable movement may be haphazard and
irregular.
These findings and methodological applications highlight some complexities observed within
the ECOWAS monetary region. This exposes areas of weaknesses or policy impacts within the system
and can assist in the development of policy responses to support integration (such as the post-Covid-
19 economic recovery), and in the long run, minimise individual risk of asymmetry shocks within the
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region. Other areas of possible application could focus on integration into the wider continental
agreements (e.g., the African Continental Free Trade Area agreement).
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Annex
Annex 1.1. Configurational Model for Emergent Clusters
Note: For cases where the achievement of the convergence criteria is met, data is shaded in the
colour of the pattern in the HCA results. Likewise, in cases where this is demonstrated across a set of
convergent cases, indicators are in “Bold” and “Italic.” Variable codes: Primary Criteria: BD - Ratio of
Budget Deficit to Nominal GDP; IR - Annual Average Inflation Rate; CBF - Central Bank Financing of
Budget Deficit; GER - Gross External Reserve. Secondary Criteria: TPD - Ratio of Total Public Debt to
GDP; NERV - Nominal Exchange Rate Variation.
BD_2012
IR_2012
CBF_2012
GER_2012
TPD_2012
NERV_2012
Nigeria
-1.40
12.20
0
8.50
12.60
0.70
Cote D'Ivoire
3.20
1.30
0
5.30
34.20
-4.80
Guinea
Bissau
2.10
2.10
0
5.30
52.40
-4.80
BD_2013
IR_2013
CBF_2013
GER_2013
TPD_2013
NERV_2013
Nigeria
-1.3
8.5
0
8.9
12.4
2.1
Cote D'Ivoire
2.2
2.6
0
4.7
34
4.1
Guinea
Bissau
3.4
0.7
0
4.7
52.6
4.1
BD_2014
IR_2014
CBF_2014
GER_2014
TPD_2014
NERV_2014
Nigeria
-1
8
0
6
12.5
-1.9
Cote D'Ivoire
2.2
0.4
0
5
36.9
0.1
Guinea
Bissau
2.6
-1
0
5
53.3
0.1
BD_2015
IR_2015
CBF_2015
GER_2015
TPD_2015
NERV_2015
Nigeria
-1.5
9
13.1
8.2
12.6
-1.9
Cote D'Ivoire
2.9
1.2
0
5
40.8
-9.3
Guinea
Bissau
2.7
1.4
0
5
46.8
-9.3
BD_2016
IR_2016
CBF_2016
GER_2016
TPD_2016
NERV_2016
Nigeria
-2.2
15.7
0
5.8
17.1
-23.5
Cote D'Ivoire
3.9
0.7
0
4.4
42.1
0.5
Guinea
Bissau
4
1.5
0
4.4
46.1
0.5
Table 5. Configurational model for Nigeria, Cote D’Ivoire, and Guinea Bissau
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BD_2012
IR_201
2
CBF_201
2
GER_201
2
TPD_201
2
NERV_201
2
Senegal
-5.80
1.40
0
5.30
36.70
-4.80
Togo
-5.80
2.60
0
5.30
44.00
-4.80
BD_2013
IR_201
3
CBF_201
3
GER_201
3
TPD_201
3
NERV_201
3
Senegal
-5.5
0.7
0
4.7
30.7
4.1
Togo
-4.6
1.8
0
4.7
45.3
4.1
BD_2014
IR_201
4
CBF_201
4
GER_201
4
TPD_201
4
NERV_201
4
Senegal
-5.2
1.1
0
5
35.4
0.1
Togo
-3.4
0.2
0
5
66.9
0.1
BD_2015
IR_201
5
CBF_201
5
GER_201
5
TPD_201
5
NERV_201
5
Senegal
-4.8
0.1
0
5
29.1
-9.3
Togo
-6.3
1.8
0
5
76.8
-9.3
BD_2016
IR_201
6
CBF_201
6
GER_201
6
TPD_201
6
NERV_201
6
Senegal
-4.2
0.8
0
5.79
55.7
0.5
Togo
-8.5
0.9
0
4.4
79.4
0.5
Table 6. Configurational model for Senegal and Togo
BD_2012
IR_2012
CBF_2012
GER_2012
TPD_2012
NERV_2012
Ghana
-5.70
9.10
25.40
3.00
47.80
-4.40
Cabo Verde
-12.40
2.50
0
4.00
91.10
-4.00
Gambia
-4.60
4.30
0.40
4.80
78.00
-4.50
BD_2013
IR_2013
CBF_2013
GER_2013
TPD_2013
NERV_2013
Ghana
-8.6
11.7
12.3
3.1
56.8
-7.4
Cabo Verde
-8.8
1.5
0
4.9
102.5
4.1
Gambia
-8.7
5.7
0
4.6
88.1
-10.3
BD_2014
IR_2014
CBF_2014
GER_2014
TPD_2014
NERV_2014
Ghana
-6.4
17
13.7
3
70.2
-31.5
Cabo Verde
-7.2
-0.2
0
5.4
115
0.1
Gambia
-9.6
6.9
40.8
3.7
104.1
-16.5
BD_2015
IR_2015
CBF_2015
GER_2015
TPD_2015
NERV_2015
Ghana
-4.8
17.2
4.1
2.6
73.2
-15.7
Cabo Verde
-3.9
0.1
0
6.4
126.1
-9.3
Gambia
-6.3
6.8
41.5
2.5
101.1
4.9
BD_2016
IR_2016
CBF_2016
GER_2016
TPD_2016
NERV_2016
Ghana
-10.9
17.5
0
2.8
73.1
-4.2
Cabo Verde
-3.5
-1.4
0
6.6
128.6
0.5
Gambia
-9.5
7.9
33.1
2.4
117.3
-3.3
Table 7. Configurational model for Cluster Ghana, Cabo Verde, and Gambia
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BD_2012
IR_2012
CBF_2012
GER_2012
TPD_2012
NERV_2012
Liberia
7.50
6.90
0
2.80
34.10
1.30
Guinea
3.20
15.20
0
2.40
42.20
-2.50
Sierra Leone
-5.20
12.90
- 37.70
3.40
36.70
3.30
BD_2013
IR_2013
CBF_2013
GER_2013
TPD_2013
NERV_2013
Liberia
-0.5
7.6
0
2.8
30.5
-4.1
Guinea
-2
11.9
0.01
2.9
44.5
2.1
Sierra Leone
-1.6
10.4
1.7
3.2
30.8
1.1
BD_2014
IR_2014
CBF_2014
GER_2014
TPD_2014
NERV_2014
Liberia
0.2
9.8
0
2.5
37.9
-9
Guinea
-3.56
9.7
0
3.2
73.5
-1.5
Sierra Leone
-3.3
7.2
7.2
3.6
35.4
-4
BD_2015
IR_2015
CBF_2015
GER_2015
TPD_2015
NERV_2015
Liberia
1.6
7.8
0
2.3
32
7.2
Guinea
-6.9
8.2
0.26
2.2
43.3
2.2
Sierra Leone
-4.1
8.1
-0.7
3.8
29.1
-3.1
BD_2016
IR_2016
CBF_2016
GER_2016
TPD_2016
NERV_2016
Liberia
2.2
8.8
0
3.3
36.7
-8.4
Guinea
0.1
8.2
0.01
1.4
43.1
-16.4
Sierra Leone
-6.4
10.8
33.1
4.7
55.7
-19.1
Table 8. Configurational model for Liberia, Guinea, and Sierra Leone
BD_2012
IR_2012
CBF_2012
GER_2012
TPD_2012
NERV_2012
Benin
-0.40
6.80
0
5.30
26.80
-4.80
Niger
-1.10
0.50
0
5.30
18.80
-4.80
BD_2013
IR_2013
CBF_2013
GER_2013
TPD_2013
NERV_2013
Benin
-2.6
1
0
4.7
25.4
4.1
Niger
-2.6
2.3
0
4.7
23.1
4.1
BD_2014
IR_2014
CBF_2014
GER_2014
TPD_2014
NERV_2014
Benin
-1.9
-1.1
0
5
30.9
0.1
Niger
-8.1
0.9
0
5
25.6
0.1
BD_2015
IR_2015
CBF_2015
GER_2015
TPD_2015
NERV_2015
Benin
-8
0.3
0
5
42.4
-9.3
Niger
-9
1
0
5
36
-9.3
BD_2016
IR_2016
CBF_2016
GER_2016
TPD_2016
NERV_2016
Benin
-6.2
-0.8
0
4.4
49.4
0.5
Niger
-6.1
0.2
0
4.4
39.7
0.5
Table 9. Configurational model for Benin and Niger
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BD_2012
IR_2012
CBF_2012
GER_2012
TPD_2012
NERV_2012
Burkina Faso
-3.10
3.80
0
5.30
27.96
4.78
Mali
-0.10
5.30
0
5.30
24.30
4.80
BD_2013
IR_2013
CBF_2013
GER_2013
TPD_2013
NERV_2013
Burkina Faso
-3.58
0.5
0
4.7
28.58
4.12
Mali
-2.2
0.6
0
4.7
26
4.1
BD_2014
IR_2014
CBF_2014
GER_2014
TPD_2014
NERV_2014
Burkina Faso
-1.85
-0.3
0
5
30.79
0.09
Mali
-3.8
0.9
0
5
27.1
0.1
BD_2015
IR_2015
CBF_2015
GER_2015
TPD_2015
NERV_2015
Burkina Faso
-2.03
0.9
0
5
32.75
9.33
Mali
-1.8
1.5
0
5
30.8
9.3
BD_2016
IR_2016
CBF_2016
GER_2016
TPD_2016
NERV_2016
Burkina Faso
-3.14
-0.2
0
4.4
34.23
0.5
Mali
-3.9
1.8
0
4.4
36
0.5
Table 10. Configurational model for Burkina Faso and Mali
Annex 1.2. Correlation Plot for 2012 to 2015
Note: Correlograms display correlation coefficients for variable pairs. Pairs with more intense colours
have more extreme correlations. For insignificant correlations (not significantly different from 0),
these are represented by a white box. For more details, see Friendly (2002). Variable codes: Primary
Criteria: BD – Ratio of Budget Deficit to Nominal GDP; IR – Annual Average Inflation Rate; CBF –
Central Bank Financing of Budget Deficit; GER – Gross External Reserve. Secondary Criteria: TPD –
Ratio of Total Public Debt to GDP; NERV – Nominal Exchange Rate Variation.
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Annex 1.2. Correlation Plot for 2016 (Correlograms – see Friendly, 2002) (Continued)
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Annex 1.3. Trends in Convergence Indicators
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Annex 1.3. Trends in Convergence Indicators (Continued)
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Annex 1.3. Trends in Convergence Indicators (Continued)
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DOI: http://dx.doi.org/10.20377/cgn- 108
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Appendix 1.4. ECOWAS Convergence Situation 2012 - 2016 Dataset7
Variable codes: Primary Criteria: BD - Ratio of Budget Deficit to Nominal GDP; IR - Annual Average
Inflation Rate; CBF - Central Bank Financing of Budget Deficit; GER - Gross External Reserve.
Secondary Criteria: TPD - Ratio of Total Public Debt to GDP; NERV - Nominal Exchange Rate
Variation.
7 Source: Economic Community of West African States. (2017). 2016 ECOWAS convergence report. Abuja: ECOWAS.
Country
BD_2012
IR_2012
CBF_2012
GER_2012
TPD_2012
NERV_2012
Benin
- 0.40
6.80
0
5.30
26.80
- 4.80
Burkina Faso
- 3.10
3.80
0
5.30
27.96
4.78
Cabo Verde
-12.40
2.50
0
4.00
91.10
- 4.00
Cote D'Ivoire
3.20
1.30
0
5.30
34.20
- 4.80
Gambia
- 4.60
4.30
0.40
4.80
78.00
- 4.50
Ghana
- 5.70
9.10
25.40
3.00
47.80
- 4.40
Guinea
3.20
15.20
0
2.40
42.20
- 2.50
Guinea Bissau
2.10
2.10
0
5.30
52.40
- 4.80
Liberia
7.50
6.90
0
2.80
34.10
1.30
Mail
- 0.10
5.30
0
5.30
24.30
4.80
Niger
- 1.10
0.50
0
5.30
18.80
- 4.80
Nigeria
- 1.40
12.20
0
8.50
12.60
0.70
Senegal
- 5.80
1.40
0
5.30
36.70
- 4.80
Sierra Leone
- 5.20
12.90
- 37.70
3.40
36.70
3.30
Togo
- 5.80
2.60
0
5.30
44.00
- 4.80
Complexity, Governance & Networks – Vol. 7, No 1 (2021) Special Issue: Complexity and Time in Governance, p. 50-78
DOI: http://dx.doi.org/10.20377/cgn- 108
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Appendix 1.4. ECOWAS Convergence Situation 2012 - 2016 Dataset (Continued)8
Country
BD_2013
IR_2013
CBF_2013
GER_2013
TPD_2013
NERV_2013
Benin
-2.6
1
0
4.7
25.4
4.1
Burkina
Faso
-3.58
0.5
0
4.7
28.58
4.12
Cabo
Verde
-8.8
1.5
0
4.9
102.5
4.1
Cote
D'Ivoire
2.2
2.6
0
4.7
34
4.1
Gambia
-8.7
5.7
0
4.6
88.1
-10.3
Ghana
-8.6
11.7
12.3
3.1
56.8
-7.4
Guinea
-2
11.9
0.01
2.9
44.5
2.1
Guinea
Bissau
3.4
0.7
0
4.7
52.6
4.1
Liberia
-0.5
7.6
0
2.8
30.5
-4.1
Mail
-2.2
0.6
0
4.7
26
4.1
Niger
-2.6
2.3
0
4.7
23.1
4.1
Nigeria
-1.3
8.5
0
8.9
12.4
2.1
Senegal
-5.5
0.7
0
4.7
30.7
4.1
Sierra
Leone
-1.6
10.4
1.7
3.2
30.8
1.1
Togo
-4.6
1.8
0
4.7
45.3
4.1
Variable codes: Primary Criteria: BD - Ratio of Budget Deficit to Nominal GDP; IR - Annual Average
Inflation Rate; CBF - Central Bank Financing of Budget Deficit; GER - Gross External Reserve.
Secondary Criteria: TPD - Ratio of Total Public Debt to GDP; NERV - Nominal Exchange Rate
Variation.
8 Source: Economic Community of West African States. (2017). 2016 ECOWAS convergence report. Abuja: ECOWAS.
Complexity, Governance & Networks – Vol. 7, No 1 (2021) Special Issue: Complexity and Time in Governance, p. 50-78
DOI: http://dx.doi.org/10.20377/cgn- 108
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Appendix 1.4. ECOWAS Convergence Situation 2012 - 2016 Dataset (Continued)
Country
BD_2014
IR_2014
CBF_2014
GER_2014
TPD_2014
NERV_2014
BD_2015
IR_2015
CBF_2015
GER_2015
TPD_2015
NERV_2015
Benin
-1.9
-1.1
0
5
30.9
0.1
-8
0.3
0
5
42.4
-9.3
Burkina Faso
-1.85
-0.3
0
5
30.79
0.09
-2.03
0.9
0
5
32.75
9.33
Cabo Verde
-7.2
-0.2
0
5.4
115
0.1
-3.9
0.1
0
6.4
126.1
-9.3
Cote D'Ivoire
2.2
0.4
0
5
36.9
0.1
2.9
1.2
0
5
40.8
-9.3
Gambia
-9.6
6.9
40.8
3.7
104.1
-16.5
-6.3
6.8
41.5
2.5
101.1
4.9
Ghana
-6.4
17
13.7
3
70.2
-31.5
-4.8
17.2
4.1
2.6
73.2
-15.7
Guinea
-3.56
9.7
0
3.2
73.5
-1.5
-6.9
8.2
0.26
2.2
43.3
2.2
Guinea Bissau
2.6
-1
0
5
53.3
0.1
2.7
1.4
0
5
46.8
-9.3
Liberia
0.2
9.8
0
2.5
37.9
-9
1.6
7.8
0
2.3
32
7.2
Mail
-3.8
0.9
0
5
27.1
0.1
-1.8
1.5
0
5
30.8
9.3
Niger
-8.1
0.9
0
5
25.6
0.1
-9
1
0
5
36
-9.3
Nigeria
-1
8
0
6
12.5
-1.9
-1.5
9
13.1
8.2
12.6
-1.9
Senegal
-5.2
1.1
0
5
35.4
0.1
-4.8
0.1
0
5
29.1
-9.3
Sierra Leone
-3.3
7.2
7.2
3.6
35.4
-4
-4.1
8.1
-0.7
3.8
29.1
-3.1
Togo
-3.4
0.2
0
5
66.9
0.1
-6.3
1.8
0
5
76.8
-9.3
Variable codes: Primary Criteria: BD - Ratio of Budget Deficit to Nominal GDP; IR - Annual Average
Inflation Rate; CBF - Central Bank Financing of Budget Deficit; GER - Gross External Reserve.
Secondary Criteria: TPD - Ratio of Total Public Debt to GDP; NERV - Nominal Exchange Rate
Variation.
Complexity, Governance & Networks – Vol. 7, No 1 (2021) Special Issue: Complexity and Time in Governance, p. 50-78
DOI: http://dx.doi.org/10.20377/cgn- 108
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University of Bamberg Press
Appendix 1.4. ECOWAS Convergence Situation 2012 - 2016 Dataset (Continued)
Variable codes: Primary Criteria: BD - Ratio of Budget Deficit to Nominal GDP; IR - Annual Average
Inflation Rate; CBF - Central Bank Financing of Budget Deficit; GER - Gross External Reserve.
Secondary Criteria: TPD - Ratio of Total Public Debt to GDP; NERV - Nominal Exchange Rate
Variation.
Country
BD_2016
IR_2016
CBF_2016
GER_2016
TPD_2016
NERV_201
6
Benin
-6.2
-0.8
0
4.4
49.4
0.5
Burkina Faso
-3.14
-0.2
0
4.4
34.23
0.5
Cabo Verde
-3.5
-1.4
0
6.6
128.6
0.5
Cote D'Ivoire
3.9
0.7
0
4.4
42.1
0.5
Gambia
-9.5
7.9
33.1
2.4
117.3
-3.3
Ghana
-10.9
17.5
-
2.8
73.1
-4.2
Guinea
0.1
8.2
0.01
1.4
43.1
-16.4
Guinea Bissau
4
1.5
0
4.4
46.1
0.5
Liberia
2.2
8.8
0
3.3
36.7
-8.4
Mail
-3.9
1.8
0
4.4
36
0.5
Niger
-6.1
0.2
0
4.4
39.7
0.5
Nigeria
-2.2
15.7
0
5.8
17.1
-23.5
Senegal
-4.2
0.8
0
5.79
55.7
0.5
Sierra Leone
-6.4
10.8
33.1
4.7
55.7
-19.1
Togo
-8.5
0.9
0
4.4
79.4
0.5
Complexity, Governance & Networks – Vol. 7, No 1 (2021) Special Issue: Complexity and Time in Governance, p. 50-78
DOI: http://dx.doi.org/10.20377/cgn- 108
